Inferring Multitasking Breakpoints from Single-Task Data

Recent research has shown that computer users placed in a deferrable multitasking situation generally postpone secondarytask interruptions until points of low mental workload in the primary task. Studies examining this phenomenon have relied on empirical data that explicitly show user switch points in the course of multitask performance. This paper addresses a related question: Can these same switch points, found empirically in a multitasking context, be inferred solely from single-task data? We investigate this question and propose an approach that analyzes a particular behavioral signature in single-task data—outliers in the distributions of time between task actions—to infer multitasking breakpoints. We evaluate this approach using behavioral data from a user-interface task, showing how the proposed method’s inferences from singletask data match well to the real switch points observed during multitask performance

Finding Canonical Behaviors in User Protocols

While the collection of behavioral protocols has been common practice in human-computer interaction research for many years, the analysis of large protocol data sets is often extremely tedious and time-consuming, and automated analysis methods have been slow to develop. This paper proposes an automated method of protocol analysis to find canonical behaviors — a small subset of protocols that is most representative of the full data set, providing a reasonable “big picture ” view of the data with as few protocols as possible. The automated method takes advantage of recent algorithmic developments in computational vision, modifying them to allow for distance measures between behavioral protocols. The paper includes an application of the method to web-browsing protocols, showing how the canonical behaviors found by the method match well to sets of behaviors identified by expert human coders